Role: Data Analyst I – Operations Analytics
Experience: 0–2 Years
Extension: Subject to irregular operations (IROPs) analytics backlog, seasonal schedule volatility, and expansion of delay attribution models within NOC
Work Mode: Remote (5 days/week)
Employment Type: W2
Work Authorization: CPT / OPT
Additional Notes
- This is a production-facing analytics role—not a reporting-only position
- Candidates will be expected to support live operational decision-making during flight windows
- Shift flexibility is required based on airline schedule cycles
- No third-party layers; direct client interaction post-selection
Key Responsibilities
- Perform daily delay attribution analysis by reconciling actual vs scheduled timestamps across gate-out, wheels-off, wheels-on, and gate-in events
- Build and maintain SQL-based pipelines to extract operational data from internal aviation systems (movement logs, crew scheduling feeds, maintenance events)
- Support turnaround time (TAT) analysis across key stations; identify bottlenecks impacting aircraft utilization
- Generate hourly and daily operational dashboards used by NOC leadership during active flight windows
- Work with Dispatch and Crew teams to validate data discrepancies during irregular operations (IROPs)
- Assist in root cause analysis for cancellations and diversions using structured and semi-structured datasets
- Develop basic forecasting models (Excel/Python) for delay propagation across connecting flights
- Maintain data quality checks across multiple upstream systems; flag inconsistencies impacting KPI reporting
- Document business logic for delay codes, cancellation categories, and operational KPIs
Required Qualifications
- Bachelor’s degree in Data Analytics, Statistics, Industrial Engineering, Aviation Management, or related field
- 0–2 years of hands-on experience (internship, academic projects, or full-time) working with structured datasets
- Strong SQL skills (joins, aggregations, window functions required; subqueries expected)
- Proficiency in Excel (pivot tables, lookups, basic data modeling)
- Understanding of data reconciliation and data validation concepts
- Ability to work in a real-time operations environment (including early morning or evening overlap shifts)
- Strong attention to detail—errors directly impact operational decision-making
Preferred Qualifications
- Exposure to aviation datasets (flight schedules, delay codes, tail tracking)
- Python (Pandas, NumPy) for data manipulation
- Familiarity with BI tools (Tableau or Power BI)
- Understanding of time-series data and event-based datasets
- Prior exposure to operations-heavy environments (logistics, airlines, supply chain)